from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2021-12-01 14:04:06.658492
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'1. Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64('2020-12-06'),
'red', 'inside top left'),
'2. Soft Lockdown': (np.datetime64('2020-12-06'), np.datetime64('2020-12-27'),
'orange', 'inside top left'),
'Weihnachten 2020': (np.datetime64('2020-12-24'), np.datetime64('2020-12-27'),
'blue', 'inside top left'),
'3. Lockdown': (np.datetime64('2020-12-27'), np.datetime64(TODAY),
'red', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Wed, 01, Dec, 2021
Time: 14:04:12
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -47.3020
Nobs: 492.000 HQIC: -47.7684
Log likelihood: 5632.16 FPE: 1.32886e-21
AIC: -48.0700 Det(Omega_mle): 1.10874e-21
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.383906 0.083157 4.617 0.000
L1.Burgenland 0.094994 0.044571 2.131 0.033
L1.Kärnten -0.116300 0.022863 -5.087 0.000
L1.Niederösterreich 0.165683 0.092475 1.792 0.073
L1.Oberösterreich 0.125837 0.094165 1.336 0.181
L1.Salzburg 0.281563 0.047842 5.885 0.000
L1.Steiermark 0.017316 0.061821 0.280 0.779
L1.Tirol 0.108107 0.049842 2.169 0.030
L1.Vorarlberg -0.085084 0.043922 -1.937 0.053
L1.Wien 0.031186 0.083934 0.372 0.710
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.030084 0.184713 0.163 0.871
L1.Burgenland -0.051296 0.099003 -0.518 0.604
L1.Kärnten 0.036813 0.050784 0.725 0.469
L1.Niederösterreich -0.220066 0.205412 -1.071 0.284
L1.Oberösterreich 0.472527 0.209166 2.259 0.024
L1.Salzburg 0.310876 0.106269 2.925 0.003
L1.Steiermark 0.099514 0.137321 0.725 0.469
L1.Tirol 0.307207 0.110711 2.775 0.006
L1.Vorarlberg 0.008866 0.097563 0.091 0.928
L1.Wien 0.016605 0.186440 0.089 0.929
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.233796 0.042285 5.529 0.000
L1.Burgenland 0.091985 0.022664 4.059 0.000
L1.Kärnten -0.004484 0.011626 -0.386 0.700
L1.Niederösterreich 0.221799 0.047023 4.717 0.000
L1.Oberösterreich 0.162901 0.047882 3.402 0.001
L1.Salzburg 0.034716 0.024327 1.427 0.154
L1.Steiermark 0.025597 0.031436 0.814 0.415
L1.Tirol 0.075295 0.025344 2.971 0.003
L1.Vorarlberg 0.056278 0.022334 2.520 0.012
L1.Wien 0.103981 0.042680 2.436 0.015
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.173270 0.041100 4.216 0.000
L1.Burgenland 0.043457 0.022029 1.973 0.049
L1.Kärnten -0.012301 0.011300 -1.089 0.276
L1.Niederösterreich 0.149792 0.045706 3.277 0.001
L1.Oberösterreich 0.339867 0.046541 7.302 0.000
L1.Salzburg 0.098484 0.023646 4.165 0.000
L1.Steiermark 0.107817 0.030555 3.529 0.000
L1.Tirol 0.086072 0.024634 3.494 0.000
L1.Vorarlberg 0.054349 0.021709 2.504 0.012
L1.Wien -0.040788 0.041485 -0.983 0.326
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.179690 0.079398 2.263 0.024
L1.Burgenland -0.041415 0.042556 -0.973 0.330
L1.Kärnten -0.036152 0.021829 -1.656 0.098
L1.Niederösterreich 0.123475 0.088295 1.398 0.162
L1.Oberösterreich 0.177311 0.089909 1.972 0.049
L1.Salzburg 0.252898 0.045679 5.536 0.000
L1.Steiermark 0.074653 0.059027 1.265 0.206
L1.Tirol 0.130554 0.047589 2.743 0.006
L1.Vorarlberg 0.107172 0.041937 2.556 0.011
L1.Wien 0.035888 0.080140 0.448 0.654
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.082750 0.062973 1.314 0.189
L1.Burgenland 0.015394 0.033752 0.456 0.648
L1.Kärnten 0.051432 0.017314 2.971 0.003
L1.Niederösterreich 0.175098 0.070030 2.500 0.012
L1.Oberösterreich 0.337230 0.071310 4.729 0.000
L1.Salzburg 0.050313 0.036229 1.389 0.165
L1.Steiermark -0.006026 0.046816 -0.129 0.898
L1.Tirol 0.123169 0.037744 3.263 0.001
L1.Vorarlberg 0.058738 0.033261 1.766 0.077
L1.Wien 0.113741 0.063562 1.789 0.074
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.179717 0.076446 2.351 0.019
L1.Burgenland 0.011600 0.040974 0.283 0.777
L1.Kärnten -0.060627 0.021018 -2.885 0.004
L1.Niederösterreich -0.113163 0.085012 -1.331 0.183
L1.Oberösterreich 0.224411 0.086566 2.592 0.010
L1.Salzburg 0.036744 0.043980 0.835 0.403
L1.Steiermark 0.266289 0.056832 4.686 0.000
L1.Tirol 0.489359 0.045819 10.680 0.000
L1.Vorarlberg 0.072409 0.040377 1.793 0.073
L1.Wien -0.103465 0.077160 -1.341 0.180
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.138337 0.084562 1.636 0.102
L1.Burgenland -0.013395 0.045324 -0.296 0.768
L1.Kärnten 0.064092 0.023249 2.757 0.006
L1.Niederösterreich 0.172264 0.094039 1.832 0.067
L1.Oberösterreich -0.075395 0.095757 -0.787 0.431
L1.Salzburg 0.222242 0.048650 4.568 0.000
L1.Steiermark 0.134528 0.062866 2.140 0.032
L1.Tirol 0.050701 0.050684 1.000 0.317
L1.Vorarlberg 0.142550 0.044665 3.192 0.001
L1.Wien 0.167813 0.085353 1.966 0.049
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.461066 0.046661 9.881 0.000
L1.Burgenland -0.000876 0.025010 -0.035 0.972
L1.Kärnten -0.013185 0.012829 -1.028 0.304
L1.Niederösterreich 0.179096 0.051890 3.451 0.001
L1.Oberösterreich 0.263980 0.052838 4.996 0.000
L1.Salzburg 0.018480 0.026845 0.688 0.491
L1.Steiermark -0.014224 0.034689 -0.410 0.682
L1.Tirol 0.069764 0.027967 2.495 0.013
L1.Vorarlberg 0.056603 0.024646 2.297 0.022
L1.Wien -0.018747 0.047097 -0.398 0.691
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.025623 0.089072 0.152599 0.137030 0.062557 0.081456 0.015327 0.206844
Kärnten 0.025623 1.000000 -0.038173 0.126631 0.046721 0.072581 0.456495 -0.082245 0.093621
Niederösterreich 0.089072 -0.038173 1.000000 0.275915 0.094468 0.252863 0.046018 0.142143 0.241804
Oberösterreich 0.152599 0.126631 0.275915 1.000000 0.186652 0.284442 0.160243 0.125226 0.174852
Salzburg 0.137030 0.046721 0.094468 0.186652 1.000000 0.119795 0.058347 0.109472 0.060610
Steiermark 0.062557 0.072581 0.252863 0.284442 0.119795 1.000000 0.131127 0.087559 0.004143
Tirol 0.081456 0.456495 0.046018 0.160243 0.058347 0.131127 1.000000 0.062831 0.128485
Vorarlberg 0.015327 -0.082245 0.142143 0.125226 0.109472 0.087559 0.062831 1.000000 -0.011958
Wien 0.206844 0.093621 0.241804 0.174852 0.060610 0.004143 0.128485 -0.011958 1.000000